View source for 18431180. Token Packing for Sequence Models simplified abstract (Microsoft Technology Licensing, LLC)

=Token Packing for Sequence Models=


==Organization Name==

[[:Category:Microsoft Technology Licensing, LLC|Microsoft Technology Licensing, LLC]]


[[Category:Microsoft Technology Licensing, LLC]]

==Inventor(s)==

[[:Category:Andy Wagner of Cupertino CA (US)|Andy Wagner of Cupertino CA (US)]][[Category:Andy Wagner of Cupertino CA (US)]]

[[:Category:Tiyasa Mitra of San Jose CA (US)|Tiyasa Mitra of San Jose CA (US)]][[Category:Tiyasa Mitra of San Jose CA (US)]]

[[:Category:Marc Tremblay of Bellevue WA (US)|Marc Tremblay of Bellevue WA (US)]][[Category:Marc Tremblay of Bellevue WA (US)]]

==Token Packing for Sequence Models - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18431180 titled 'Token Packing for Sequence Models

==Simplified Explanation==

Embodiments of the present disclosure include systems and methods for packing tokens to train sequence models. In some embodiments, a plurality of datasets for training a sequence model is received. Each dataset in the plurality of datasets includes a sequence of correlated tokens. A set of training data is generated that includes a subset of a sequence of tokens from a first dataset in the plurality of datasets and a subset of a sequence of tokens from a second, different dataset in the plurality of datasets. The sequence model is trained using the set of training data.

* Systems and methods for packing tokens to train sequence models
* Receive multiple datasets for training a sequence model, each containing a sequence of correlated tokens
* Generate a set of training data by combining subsets of tokens from different datasets
* Train the sequence model using the set of training data

== Potential Applications ==
This technology could be applied in:
* Natural language processing
* Speech recognition systems
* Machine translation algorithms

== Problems Solved ==
This technology helps to:
* Improve the accuracy of sequence models
* Enhance the performance of machine learning algorithms
* Optimize training processes for large datasets

== Benefits ==
The benefits of this technology include:
* Increased efficiency in training sequence models
* Enhanced predictive capabilities in various applications
* Better utilization of correlated token data for training

== Potential Commercial Applications ==
This technology has potential commercial applications in:
* Software development companies
* Data analytics firms
* Artificial intelligence research organizations

== Possible Prior Art ==
One possible prior art for this technology could be:
* Existing methods for training sequence models using token data

== Unanswered Questions ==
=== How does this technology compare to existing token packing methods in terms of efficiency and accuracy? ===
This article does not provide a direct comparison with existing token packing methods to evaluate efficiency and accuracy.

=== Are there any limitations or constraints in the implementation of this technology in real-world applications? ===
The article does not address any potential limitations or constraints that may arise when implementing this technology in real-world scenarios.


==Original Abstract Submitted==

Embodiments of the present disclosure include systems and methods for packing tokens to train sequence models. In some embodiments, a plurality of datasets for training a sequence model is received. Each dataset in the plurality of datasets includes a sequence of correlated tokens. A set of training data is generated that includes a subset of a sequence of tokens from a first dataset in the plurality of datasets and a subset of a sequence of tokens from a second, different dataset in the plurality of datasets. The sequence model is trained using the set of training data.



[[Category:G06F40/284]]
[[Category:G06N3/044]]
[[Category:G06N3/045]]
[[Category:G06N3/08]]
[[Category:G06N20/00]]
[[Category:G10L15/06]]